The need for collecting information about the user's ambient environment is essential to automated algorithms that deduce the user's current context. Determining that context is important for next generation user experiences. For example, a restaurant locating app may return a different result if a user is with a spouse versus if a user is with a co-worker. A user's ambient environment includes the unique identifiers that can be sensed in use in proximity to the user's device. Uses of those unique identifiers being sent by a transmitter with a static physical location are well known.
A need exists for sensing the unique identifiers of mobile device being carried on the person of individuals in proximity to the user and being able to determine the frequency and time of day that the user is in proximity with the people he/she interacts with. This information is very valuable, for example, in determining a metric called “social distance.” Social distance may be used to estimate how close your ties are with that individual and whether they are professional and or personal (e.g. which coworkers your regularly go to lunch with).
To sense unique identifiers in proximity to a user's mobile device, RF based communications methods may be utilized. Mobile devices incorporate a number of RF-based digital communications methods. Examples include various cellular voice and data technologies, Bluetooth, Wi-Fi, and proprietary standards such as ANT. These standards broadcast the signal in an omni-directional pattern, since there is no fixed relationship between the transmitter and receiver, and therefore any appropriately equipped receiver within range can receive the signal. These RF-based digital communication methods incorporate two unique identifiers, so that a transmitter can indicate whom the transmission is intended for, and where the intended receiver should send the response (if any). Collecting the identifiers of RF-based devices that have a fixed physical location (cell towers, Wi-Fi access point in buildings) and correlating these with additional information about geo-location is also well known (e.g. skyhook and similar) to improve the accuracy of positioning, particularly indoors. This may be a database of cell tower locations and triangulation, or last know GPS locations for a mobile device. Digital implementations for multi-user shared calendars are also well known and understood. Support for this functionality is also commonplace on mobile devices.
Currently, a user's mobile device may sense the unique identifiers, but does not know what individual's device the transmission is coming from. And therefore, the unique identifiers alone are not capable of providing any information that may help determine a user's current context. Furthermore, a user's mobile device may know what individuals a person is with if a calendar entry is made, but without the calendar entry, is unable to determine what individuals are in proximity.
Consequently, a need also exists for a method to utilize accessible information regarding who a user is with in conjunction with sensed unique identifiers to map those unique identifiers to specific individuals in order to provide a user's current context.